基于邻域结构的时态RDF模型及索引方法

Temporal RDF Model and Index Method Based on Neighborhood Structure

资源描述框架(Resource Description Framework,RDF)是W3C推荐的一种元数据模型和信息描述规范,已被广泛地应用于各个领域。为了跟踪RDF数据随时间的变化,将时态信息引入RDF的框架中,随着时态RDF数据的快速增长,对时态RDF数据的有效管理变得十分必要,构建合理的索引机制能够实现对数据的高效存储和查询。文中提出了一种时态RDF数据模型,给出了具体的一维编码方案,实现了简单地表示时态信息,并以较低的开销扩展现有的RDF数据模型。在此基础上,提出了基于邻域的二级索引结构。首先利用动态计数过滤器的方法索引的邻域信息,然后利用B+树索引每个结点相关的全部时态RDF数据,同时,可对大规模时态RDF数据进行更新。实验结果表明,所提方法相比对比方法在大多数情况下性能提高了35%左右,具有可扩展性和有效性。

Resource description framework(RDF)is a metadata model and information description specification recommended by W3C,which is widely used in various fields.To track changes in RDF data over time,temporal information is introduced into the RDF framework.With the rapid growth of temporal RDF data,effective management of temporal RDF data is necessary.A reasonable index mechanism can achieve efficient storage and query of data.In this paper,we first present a temporal RDF data mo-del.We propose a specific one-dimensional coding scheme,which represent temporal data simply and extend the existing RDF data model with lower overhead.Furthermore,we present its two levels of indexes based on neighborhood structure.The first one uses dynamic counting filter to index the neighborhood information of the node,and the second builds the B+tree to index the temporal RDF data related to each node.Moreover,large-scale temporal RDF data can be updated.Experimental results show that the proposed method is around 35%better than the comparison method in most cases,and it is scalable and effective.